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Neurology Publish Ahead of PrintDOI: 10.1212/WNL.0000000000012440

Diabetes Mellitus and Cognition: A Pathway Analysis in the MEMENTO Cohort

Author(s):

Eric Frison, MD PhD1,2; Cecile Proust-Lima, PhD3; Jean-Francois Mangin, PhD4,5; Marie-Odile Habert,

MD PhD4,6,7; Stephanie Bombois, MD PhD8; Pierre-Jean Ousset, MD PhD9; Florence Pasquier, MD

PhD10; Olivier Hanon, MD PhD11; Claire PAQUET, MD PhD12; Audrey GABELLE, MD PhD13;

Mathieu Ceccaldi, MD PhD14; Cédric Annweiler, MD PhD15,16; Pierre Krolak-Salmon, MD PhD17;

Yannick Béjot, MD PhD18; Catherine Belin, MD PhD19; David Wallon, MD PhD20; Mathilde Sauvee,

MD PhD21; Emilie Beaufils, MD PhD22; Isabelle Bourdel-Marchasson, MD PhD23, 24; Isabelle

Jalenques, MD PhD25; Marie Chupin, PhD4,26; Geneviève Chêne, MD PhD1,2; Carole Dufouil, PhD1,2 on behalf of the MEMENTO cohort Study Group

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Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Published Ahead of Print on July 1, 2021 as 10.1212/WNL.0000000000012440

Equal Author Contributions: Geneviève Chêne and Carole Dufouil cntributed equally to this work as senior co-authors

Corresponding Author: Carole Dufouil [email protected]

Affiliation Information for All Authors: 1. Univ. Bordeaux, Inserm, UMR 1219, Inserm, CIC1401-EC, F-33000 Bordeaux, France; 2. Pole de sante publique Centre Hospitalier Universitaire (CHU) de Bordeaux, F-33000 Bordeaux, France;3. Univ. Bordeaux, Inserm, UMR 1219, F-33000 Bordeaux, France; 4. CATI Multicenter Neuroimaging Platform, F-75000 Paris, France;5. Neurospin CEA Paris Saclay University, F-91190 Gif-sur-Yvette, France; 6. Sorbonne Université, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, LIB, F-75006, Paris, France;7. AP-HP, Hôpital Pitié-Salpêtrière, Médecine Nucléaire, F-75013, Paris, France; 8. IM2A AP-HP INSERM UMR-S975 Groupe Hospitalier Pitié-Salpêtrière Institut de la Mémoire et de la Maladie d'Alzheimer Institut du Cerveau et de la Moelle épinière Sorbonne Université Paris, France;9. Inserm UMR1027, Université de Toulouse III Paul Sabatier, F-31000 Toulouse, France; 10. Univ Lille, Inserm 1171, CHU, Centre Mémoire (CMRR) Distalz, F-59000 Lille, France;11. Service de Gériatrie, Université Paris Descartes, Hôpital Broca, F-75013 Paris, France; 12. Université de Paris, Centre de Neurologie Cognitive Hôpital Lariboisière, INSERMU1144, F-75010, Paris, France; 13. Clinical and Research Memory center of Montpellier, Department of Neurology, Gui de Chauliac Hospital, University of Montpellier, Inserm U1061, F-34000 Montpellier, France; 14. CMMR PACA Ouest CHU Timone APHM & Aix Marseille Univ INSERM INS Inst Neurosci Syst, F-13000, Marseille, France; 15. Department of Geriatric Medicine, Angers University Hospital, Angers University Memory Clinic, Research Center on Autonomy and Longevity, UPRES EA 4638, University of Angers, F-49000 Angers, France; 16. Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry, the University of Western Ontario, London, ON, Canada. 17. Univ. Lyon, Inserm U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Centre Mémoire Ressource et Recherche de Lyon (CMRR), Hôpital des Charpennes, Hospices Civils de Lyon, F-69000 Lyon, France;18. Univ. Bourgogne, EA7460, Centre Mémoire de Ressources et de Recherches, CHU Dijon Bourgogne, F-21000 Dijon, France; 19. Service de Neurologie Hôpital Saint-Louis AP-HP, F-75010 Paris, France;20. Univ. Normandie, UNIROUEN, Inserm U1245, Departement de Neurologie, CNR-MAJ, CHU de Rouen, F-76000 Rouen, France; 21. CMRR Grenoble Arc Alpin, CHU Grenoble, F-38000 Grenoble, France;22. CMRR, University Hospital Tours, F-37000 Tours, France; 23. Centre de Résonance Magnétique des Systèmes Biologiques, UMR 5536 Université de Bordeaux/CNRS, F-33000, Bordeaux, France;24. Pole de gérontologie clinique CHU de Bordeaux, F-33000 Bordeaux, France; 25. Memory Resource and Research Centre of Clermont-Ferrand, CHU de Clermont-Ferrand, and Clermont Auvergne University, F-63000 Clermont-Ferrand, France;26. Institut du Cerveau et de la Moelle épinière, Inserm, U 1127,3 CNRS, UMR 7225, Sorbonne Université, CATI, F-75013, Paris, France;

Contributions: Eric Frison: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data

Cecile Proust-Lima: Drafting/revision of the manuscript for content, including medical writing for content; Study concept or design; Analysis or interpretation of data

Jean-Francois Mangin: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design

Marie-Odile Habert: Drafting/revision of the manuscript for content, including medical writing for


content; Major role in the acquisition of data; Study concept or design

Stephanie Bombois: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Pierre-Jean Ousset: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Florence Pasquier: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Olivier Hanon: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Claire PAQUET: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Audrey GABELLE: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Mathieu Ceccaldi: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Cédric Annweiler: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Pierre Krolak-Salmon: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Yannick Béjot: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Catherine Belin: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

David Wallon:Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Mathilde Sauvee: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Emilie Beaufils: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Isabelle Bourdel-Marchasson: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Isabelle Jalenques: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data

Marie Chupin: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design

Geneviève Chêne: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data

Carole Dufouil: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data

Number of characters in title: 73

Abstract Word count: 215

Word count of main text: 3394

References: 46

Figures: 1

Tables: 5

Supplemental: STROKE


Statistical Analysis performed by: Eric Frison, MD PhD, Bordeaux University Hospital

Search Terms: [ 26 ] Alzheimer's disease, [ 36 ] Cognitive aging, [ 54 ] Cohort studies, [ 120 ] MRI, [ 122 ] PET

Acknowledgements: The MEMENTO cohort is sponsored by Bordeaux University Hospital (coordination: CIC1401-EC, Bordeaux) and was funded through research grants from the Fondation Plan Alzheimer (Alzheimer Plan 2008–2012), the French ministry of research and higher education (Plan Malandies Neurodégénératives (2016-2020)). The MEMENTO cohort has received funding support from AVID, GE Healthcare, and FUJIREBIO through private-public partnerships. The Insight-PreAD sub-study was promoted by INSERM in collaboration with the Institut du Cerveau et de la Moelle épinière, Institut Hospitalo-Universitaire, and Pfizer and has received support within the “Investissement d'Avenir” (ANR-10-AIHU-06) program. Sponsor and funders were not involved in the study conduct, analysis and interpretation of data.

Study Funding: The authors report no targeted funding

Disclosures: E Frison, C. Proust-Lima, JF Mangin, MO Habert, S Bombois, PJ Ousset, Florence Pasquier, Olivier Hanon, Claire Paquet, A Gabelle, M Ceccaldi, C Annweiler P Krolak-Salmon, Y Béjot, C Belin, D Wallon, M Sauvée, E Beaufils, I Bourdel-Marchasson, I Jalenques, M Chupin, G Chêne, C Dufouil report no disclosures relevant to the manuscript.

Appendix 2-http://links.lww.com/WNL/B459


ABSTRACT

OBJECTIVE: To assess the role of biomarkers of Alzheimer’s Disease (AD),

neurodegeneration and small vessel disease (SVD) as mediators in the association

between diabetes mellitus and cognition.

METHODS: The study sample was derived from MEMENTO, a cohort of French

adults recruited in memory clinics and screened for either isolated subjective

cognitive complaints or mild cognitive impairment. Diabetes was defined based on

blood glucose assessment, use of antidiabetic agent or self-report. We used

structural equation modelling to assess whether latent variables of AD pathology

(PET mean amyloid uptake, Aβ42/Aβ40 ratio and CSF phosphorylated tau), SVD

(white matter hyperintensities volume and visual grading), and neurodegeneration

(mean cortical thickness, brain parenchymal fraction, hippocampal volume, and

mean fluorodeoxyglucose uptake) mediate the association between diabetes and a

latent variable of cognition (five neuropsychological tests), adjusting for potential

confounders.

RESULTS: There were 254 (11.1%) participants with diabetes among 2,288

participants (median age 71.6 years; 61.8% women). The association between

diabetes and lower cognition was significantly mediated by higher

neurodegeneration (standardized indirect effect: -0.061, 95% confidence interval: -

0.089; -0.032), but not mediated by SVD and AD markers. Results were similar when

considering latent variables of memory or executive functioning.

CONCLUSION: In a large clinical cohort in the elderly, diabetes is associated with

lower cognition through neurodegeneration, independently of SVD and AD

biomarkers.


INTRODUCTION

Type 2 diabetes (diabetes) is a risk factor for cognitive decline and dementia (1,2).

Several underlying mechanisms could be involved, such as chronic hyperglycemia

leading to advanced glycation end-products, atherosclerosis, and subsequent

cerebrovascular lesions (3–5). Insulin dysregulation, including insulin resistance and

insulin deficiency, may promote cerebral hypometabolism (6) and amyloid and tau

pathologies, hallmarks of Alzheimer’s disease (AD) (7). Diabetes has also been

associated with brain structural modifications such as cerebral atrophy and

cerebrovascular lesions (8–10). Moreover, while diabetes is associated with cerebral

hypometabolism (11,12), results are conflicting regarding its association with amyloid

and tau pathology, whether measured in the brain (PET) or in CSF (11,13,14).

Previous studies have suggested a mediating role of neurodegeneration and small

vessel disease biomarkers on the association between diabetes and cognition (15–

17). However, the mediating role of AD-specific lesions (amyloid plaques and

neurofibrillary tangles), and the correlation between those different brain features

have not been considered so far.

We thus estimated the mediating effect of biomarkers of AD, neurodegeneration and

small vessel disease in the association between diabetes and cognition, in non-

demented older adults recruited from French memory clinics.

METHODS

The MEMENTO Cohort

The MEMENTO cohort is a clinic-based study of patients presenting with a large

variety of cognitive symptoms or subjective cognitive complaints, who were enrolled

between April 2011 and June 2014, within the French national network of university

hospital-based memory clinics (18). At inclusion, participants presented either 1) with

mild cognitive impairment, when performing one standard deviation worse than the

mean of the subject’s own age, sex, and education-level group, in one or more

cognitive domains, this deviation being identified for the first time through cognitive

tests performed recently (less than 6 months preceding screening phase), or 2) with

isolated cognitive complaints, if participants had subjective cognitive complaint

(assessed through visual analogic scale), without any objective cognitive deficit as

defined previously, while being 60 years and older. All participants had a Clinical


Dementia Rating scale (19) score ≤0.5. Main exclusion criteria have been described

elsewhere (22). All examinations (including neuropsychological battery

administration, clinical examinations, brain MRI, CSF samples and

fluorodeoxyglucose [FDG] and amyloid PET) followed standardized procedures (18).

Among the 2,323 participants included in the MEMENTO cohort, 2,288 participants

from 26 study centers were included in this analysis after exclusion of participants

with missing data on diabetes status (N = 35).

Standard protocol approvals, registrations, and patient consents

This study was performed in accordance with the Declaration of Helsinki. All

participants provided written informed consent. The MEMENTO cohort protocol has

been approved by the local ethics committee (“Comité de Protection des Personnes

Sud-Ouest et Outre Mer III”; approval number 2010-A01394-35) and was registered

in ClinicalTrials.gov (Identifier: NCT01926249).

Diabetes definition

Participants were classified as having diabetes at baseline visit either in presence of

fasting blood glucose ≥ 7 mmol/L (≥126 mg/dL) or non-fasting blood glucose ≥ 11.1

mmol/L (≥200 mg/dL) or antidiabetic drug intake (Anatomical Therapeutic Chemical

classification system: code A10A “insulins and analogues”, and code A10B “blood

glucose lowering drugs, excl. insulins”) or self-reported history of diabetes.

Neuropsychological evaluation

A full neuropsychological test battery was administered to participants (18). Global

cognition was assessed by Mini-Mental State Examination (MMSE) (20), long-term

memory was assessed by Free and Cued Selective Reminding Test (FCSRT) (21),

semantic verbal fluency via ‘animal’ words (22), visuo-spatial abilities by Rey-

Osterrieth Complex Figure Test (23), and attention and executive functions by Trail

Making Test (TMT) A and B (24).

Biomarkers assessment

MRI

As part of the inclusion criteria, participants had to agree to undergo brain MRI. Brain

magnetic resonance images were acquired after a standardization of the imaging

processes and coordinated by the CATI (http://cati-neuroimaging.com), a


neuroimaging platform dedicated to multicentre studies (25). Full details are

described elsewhere (18). Briefly, MRI machines of 1.5 and 3 Tesla were used

across centers using harmonized protocols. All MRI scans acquired were then

centralized, quality checked, and postprocessed to obtain standardized

measurements for each participant. Whole-brain, gray matter, and white matter

volumes were assessed with Statistical Parametric Mapping 8 (26), hippocampal

volumes with the SACHA software (27), and mean cortical thickness of each

hemisphere with FreeSurfer 5.3 averaged in the ROI of the Desikan-Killiany atlas

(28). White matter lesions volumetry was performed using WHASA software (29)

complemented by a centralized visual assessment by a trained rater using the

Fazekas and Schmidt scale (30).

FDG-PET

18F-FDG-PET was offered to all participants but was not mandatory. PET images

were acquired after a standardization of the acquisition and reconstruction imaging

parameters, coordinated by the CATI (31). After a centralized quality check and

postprocessing performed by the CATI, the following measures were obtained: mean

FDG-PET uptake for the regions of interest (ROIs) of the Automated Anatomical

Labeling atlas relative to the pons reference region (32), including partial volume

correction, and mean FDG-PET uptake for a set of AD-specific ROIs inferred from

the Alzheimer's Disease Neuroimaging Initiative database (33), expressed as

standard uptake value ratios (SUVRs).

PET amyloid imaging

PET amyloid imaging was available for 643 participants of the analytical sample,

using either 18F-florbetapir (Amyvid®, Eli Lilly) (N=437) or 18F-flutemetamol

(Vizamyl®, GE Healthcare) (N=206) radioligands. Mean brain amyloid SUVR was

computed, harmonized across the radioligands (34), and used for the current study.

CSF sampling

Lumbar puncture was offered to all participants but was not mandatory , and CSF

centralized measurements of amyloid-β 42 peptide (Aβ42), Aβ40, total tau, and

phosphorylated tau levels were performed using the standardised INNOTEST

sandwich ELISA (Fujirebio, Ghent, Belgium).

Potential confounding factors


Sociodemographic information recorded at baseline included age, sex and education

(low education defined as no or primary school, intermediate education defined as

secondary school or high school, and high education defined as university). Lifestyle

factors included smoking status (never, former and current smoker) and current

alcohol consumption (no, ≤ 1 drink/day, and >1 drink/day). Hypertension was defined

as antihypertensive drug intake or mean of three blood pressure measurements

either ≥ 140 mmHg for systolic blood pressure or ≥ 90 mmHg for diastolic blood

pressure. Dyslipidemia was defined by plasma cholesterol > 6.24 mmol/L or use of

any lipid-lowering drugs. Body mass index (BMI) was categorized as <20 kg/m², 20

to 25 kg/m², 25.1-29.9kg/m² and ≥30kg/m². History of cardiovascular disease was

defined as a self-reported history of myocardial infarction, angina pectoris, coronary

artery, or peripheral artery disease. History of stroke was self-reported. Depression

was assessed with the Neuropsychiatric Inventory–Clinician (NPI-C) (35). APOE ε2,

ε3, or ε4 alleles were determined for all participants by KBiosciences (Hoddesdon,

UK; www.kbioscience.co.uk) as described elsewhere (18). APOE ε4 status was

defined as presence of at least one ε4 allele versus absence.

Statistical analyses

Baseline characteristics were compared according to baseline diabetic status for the

analytical sample. We used chi-square test (or Fisher exact test when appropriate)

and Student t test (or non-parametric Mann-Whitney-Wilcoxon test when

appropriate) for categorical and continuous variables comparisons, respectively.

Brain parenchymal fraction was computed as the sum of grey matter and white

matter volumes divided by total intracranial volume. Total hippocampal volume was

computed as the sum of left and right hippocampal volumes. WMH volume and

hippocampal volume were adjusted for total intracranial volume using the residual

approach (36). Mean FDG uptake across the brain was used.

Structural equation modeling (SEM) (37) was used to examine a potential mediating

role of biomarkers respectively of AD, small vessel disease (SVD) and

neurodegeneration in the association between diabetes and cognition. SEM was

preferred over standard regression modeling for its ability to directly focus the

mediation analysis on the dimensions of interest (here cognition, SVD, AD and

neurodegeneration), and to define each dimension from several noisy observed

indicators. The observed indicators of the four latent variables of interest, namely AD

pathology, small vessel disease, neurodegeneration and cognition, are listed in


Table 1 . They were determined from the literature and validated in preliminary

separated SEM analyses. Correlated residuals were assumed between left and right

cortical thicknesses and between TMT A and TMT B scores to account for a

potential common source of measurement error. Mean brain amyloid SUVR was

normalized using a logarithmic transformation and then standardized (z-score) by

radioligand. The relationships between diabetes, potential confounders, and latent

variables of AD pathology, neurodegeneration, small vessel disease, and cognition

were modelled in the structural linear regressions. For ease of interpretation, the four

latent variables were standardized (mean 0, variance 1) so that one unit corresponds

to the standard deviation of a given dimension. The indirect effects of diabetes on

cognition through the latent dimensions were estimated with their 95% CI, using path

analysis technique (37). All linear regressions of mediators and cognition were

adjusted for the following potential confounding factors: age, sex, education (high

education versus low and intermediate), smoking status (current smoker versus

never or former smoker), alcohol consumption (>1 drink/day versus ≤1 drink/day),

hypertension, dyslipidemia, obesity (≥30kg/m²) and APOE genotype (ε4 carrier

versus ε4 non-carrier). Missing values for observed indicators of latent variables and

for confounding factors were handled using a full information maximum likelihood

approach, assuming missingness at random. The multicentric nature of the data was

accounted for and Huber-White robust standard errors were reported to correct for

the potential intra-center correlation (38). The general goodness of fit was evaluated

using robust Tucker-Lewis Index (TLI), robust Comparative Fit Index (CFI), robust

Root Mean Square Error of Approximation (RMSEA) and its 90% confidence interval,

p-value for test of close fit (null hypothesis RMSEA <0.05), and Standardized Root

Mean Square Residual (SRMR) with cut-offs recommended in the literature (39).

Several sensitivity analyses were performed. First, we used a different definition of

“diabetes” by excluding a self-reported history of diabetes. Second, additional

baseline characteristics associated with availability of MRI, FDG-TEP, amyloid-PET

and CSF data (living alone, Clinical Dementia Rating scale score, prevalent

dementia, depression, stroke history, cardiovascular history, and physical activity

expressed as metabolic equivalent of task minutes per week, Table 2 ) were used as

auxiliary variables in the estimation process under FIML to strengthen the missing at

random assumption. Third, as the mediation analysis framework makes the implicit

assumption that mediators (i.e., AD pathology, small vessel disease and

neurodegeneration) are anterior to the outcome (i.e., cognition), we tried to preserve

this assumption by excluding biomarkers measurements performed more than 6


months after cognitive assessments. Fourth, as CSF biomarkers are prone to

variability whereas brain biomarkers are indicators of accumulated burden of lesions

(40), we performed a sensitivity analysis using only brain amyloid load as indicator of

the latent variable for AD pathology. Finally, we also compared the results with those

obtained when considering interactions between diabetes and each mediator in the

main adjusted model, as recommended for mediation analysis (41).

We also explored the mediating pathways in the association of diabetes with specific

cognitive domains in separate models: a latent variable for memory (indicators: total

free recall score and verbal fluency) and a latent variable for executive functioning

(indicators: TMT A and TMT B scores).

Analyses were conducted using SAS v9.3 (SAS Institute Inc, Cary, NC, USA), and R

version 3.5.1 (42) with the lavaan package for SEM analysis (38).

Data Availability

Anonymized data will be shared by request from any qualified investigator for the

sole purpose of replicating procedures and results presented in the article and as

long as data transfer is in agreement with EU legislation on the general data

protection regulation.

RESULTS

Baseline description

Compared to participants without diabetes at baseline, participants with diabetes

(254, 11.1%) were more likely to be men, and to have lower education level. They

were also more likely to have hypertension, dyslipidemia, obesity, and history of

cardiovascular disease or stroke. Participants with diabetes had on average lower

performances on executive functions and attention, memory and semantic verbal

fluency (Table 3 ).

At baseline, 65.3% of participants with diabetes were taking antidiabetic medications

(oral antidiabetic agents, 57.5%; insulin, 13.8%). Diabetes status was solely based

on self-report in 60 (23.6%) of the diabetic participants. The median self-reported

duration of diabetes was 10.0 years (interquartile range, 4.9-19.4 years).

Diabetes, latent biomarkers and latent cognition


The model fit was adequate according to the recommended cutoffs: robust CFI =

0.951, robust TLI = 0.926, robust RMSEA = 0.040 (90% CI, 0.037; 0.042), p-value

for test of close fit = 1.00, and SRMR = 0.038. Associations between diabetes, AD

pathology, SVD, neurodegeneration and cognition are presented in Figure 1 .

Presence of diabetes was significantly associated with higher neurodegeneration but

was not significantly associated with AD pathology and SVD. Higher levels of small

vessel disease, neurodegeneration and AD pathology were independently

associated with lower cognition. Once adjusted for neurodegeneration, AD pathology

and SVD, there was no direct effect of diabetes on cognition (standardized β =

0.023, 95% CI: -0.030; 0.076, p = 0.40). Association between diabetes and lower

cognition was mainly mediated by higher neurodegeneration (standardized β = -

0.061, 95% CI: -0.089; -0.032, p < 0.001). The indirect effect of diabetes on cognition

via SVD and AD pathology were non-statistically significant (standardized β = 0.000,

95% CI: -0.004; 0.004, p = 0.98 and standardized β = -0.013, 95% CI: -0.040; 0.015,

p = 0.38, respectively).

In complementary analyses considering specific cognitive functions, associations

between diabetes and lower memory or lower executive functioning were also mainly

mediated by higher neurodegeneration (standardized β = -0.058, 95% CI: -0.088; -

0.029, p<0.001 and standardized β = -0.034, 95% CI: -0.051; -0.016, p<0.001

respectively) (Table 4 ).

Sensitivity analyses

Results were similar when excluding self-reported history from the definition of

diabetes, when adding auxiliary variables to the estimation process or when

excluding delayed measures of biomarkers (Table 5 ). When using only brain amyloid

load as indicator of the latent variable for AD pathology, the indirect pathway linking

diabetes to lower cognition through higher neurodegeneration was of similar

magnitude (standardized β = -0.066, 95% CI: -0.097; -0.034, p<0.001). Diabetes was

significantly associated with higher AD pathology (standardized β = 0.107, 95% CI:

0.021; 0.193, p = 0.01), and higher AD pathology was significantly associated with

lower cognition (standardized β = -0.144, 95% CI: -0.248; -0.039, p = 0.007). The

indirect pathway linking diabetes to lower cognition through AD pathology remained

non-statistically significant (standardized β = -0.015, 95% CI: -0.033; 0.002, p = 0.08)

though. When considering interaction between diabetes and each intermediate latent

variable, the indirect effects of diabetes on cognition via neurodegeneration

(standardized β = -0.059, 95%CI: -0.089; -0.030, p < 0.001), AD pathology


(standardized β = -0.011, 95%CI: -0.034; 0.012, p = 0.34) and SVD (standardized β

= -0.001, 95%CI: -0.006; 0.003, p = 0.54) remained virtually the same.

DISCUSSION

In a cross-sectional analysis of a large clinical cohort of participants with either

isolated cognitive complaints or mild cognitive impairment, we report that the

deleterious effect of diabetes on cognitive performances is mainly mediated through

markers of neurodegeneration whereas AD pathology (amyloid, p-Tau) or small

vessel disease pathology do not seem to play a major role.

The association between diabetes and markers of neurodegeneration such as brain

atrophy (8,12,13,43) and brain hypometabolism (11,12) has been consistently

reported in cross-sectional studies. While diabetes is a risk factor for vascular

disease and stroke, its association with subclinical cerebrovascular lesions (silent

brain infarcts, WMH, cerebral microbleeds) is uncertain (44). In the present study,

diabetes was not associated with small vessel disease, even though participants with

diabetes had more frequent self-reported history of stroke.

The mediating role of neurodegeneration and small vessel disease in the association

between diabetes and cognition has already been investigated in several studies. In

a sample of 4,206 older adults of the Age, Gene/Environment Susceptibility–

Reykjavik Study (mean age 76 years, 11% with diabetes), MRI markers of

neurodegeneration (gray matter, normal white matter, and total brain tissue volumes)

and small vessel disease (cortical infarcts, subcortical infarcts, WMLs, and CMBs)

significantly mediated the cross-sectional association of diabetes with lower

processing speed and executive function (15). In a longitudinal analysis on 817

participants from the Alzheimer’s Disease Neuroimaging Initiative cohort (mean age

75 years, 15% with diabetes) the effect of diabetes on cognitive decline up to 60

months (mean follow-up time, 30 months) was significantly mediated by baseline

cortical thickness (17). Similarly, in a sample of 448 older adults of the Swedish

National Study on Aging and Care in Kungsholmen (mean age at baseline, 72

years), a higher cardiovascular burden, including diabetes as a component, was

associated with a faster MMSE decline over 9 years; this effect being largely

mediated by brain MRI markers of atrophy (volumes of total gray matter, ventricles,

and hippocampus) and small vessel disease (volume of WMHs) (16). Nevertheless,

none of those studies accounted for AD biomarkers, unlike the present study.


Insulin resistance and associated insulin signaling impairment promote Aβ

accumulation and tau phosphorylation (7). However, no association between

diabetes and amyloid and tau biomarkers was reported in previous studies

(11,13,45). In the present study, diabetes was associated with higher brain amyloid

load measured on PET imaging, but diabetes was not associated with the latent

variable of AD pathology, which included CSF biomarkers of amyloid and tau. This

discrepancy between brain and CSF biomarkers can partly be explained by the

variability of CSF biomarkers, whereas brain biomarkers are indicators of

accumulated lesions.

Although it needs to be replicated in longitudinal studies, our finding that

neurodegeneration mediates the association between diabetes and cognitive

performances, independently of biomarkers of AD and small vessel disease supports

the hypothesis of a direct role of diabetes-related insulin resistance in the

development of cognitive impairment in older adults with diabetes. Indeed, insulin

also plays an important role in neuronal synaptic plasticity and facilitates learning

and memory in humans (4) and, therefore, impaired insulin signaling could directly

contribute to neuronal dysfunction and degeneration. As impaired insulin signaling

has also been linked to promotion of amyloid-β accumulation and tau

hyperphosphorylation (7), brain insulin resistance could be a therapeutic target in AD

and related dementias. Several exploratory clinical trials have reported a beneficial

effect on cognition of intranasal insulin for healthy participants, participants with

diabetes, mild cognitive impairment or AD (46), and longer-term trials are currently

ongoing.

The MEMENTO study has several strengths to answer the current objectives. First, a

wide range of biomarkers was acquired in a highly standardized setting on more than

2,000 participants allowing a multi-dimensional assessment of brain ageing and

pathology biomarkers. Indeed, we were able to include simultaneously brain MRI,

brain FDG-PET, amyloid-PET and CSF data in a mediation analysis of the diabetes-

cognition association, offering a unique insight on underlying mechanisms. Second,

we were able to model brain biomarkers as latent variables in a SEM framework,

accounting for measurement error of the indicators, and we were able to estimate

direct and indirect effects of diabetes on several domains of cognition. Third, results

were robust to several sensitivity analyses. There are also some limitations. First, the

temporal relationship between diabetes, biomarkers and cognition is not ensured by

the cross-sectional design, and causality cannot be claimed. Nevertheless, we can


hypothesize that diabetes preceded biomarkers measures in most participants with

diabetes (duration was 4.9 years or more in 75% of participants with diabetes). We

also modeled correlations between neurodegeneration, AD pathology and SVD

instead of directed relationships because the causal interpretation of their

interrelations requires longitudinal data. Second, no tau-PET data was available to

assess tau pathology, and we had to use CSF phosphorylated tau as a proxy for

cerebral tau accumulation, assuming a strong correlation between both, as

suggested by existing evidence (40). Third, the analytical strategy relies on the

assumption that data are missing at random. This assumption may be strong for

CSF and PET-amyloid data, for which 70% to 80% of data were missing. However,

we used a broad range of baseline characteristics associated with availability of CSF

and PET-amyloid data as auxiliary variables in the estimation process, thus making

the missing-at-random assumption more plausible. We must also acknowledge the

unavailability of data regarding past and current glucose control that prevented us to

explore whether diabetes control modified the explored relationships. Finally, the

observed findings may not fully translate in the general older population, as

participants in the MEMENTO study are adults with either isolated cognitive

complaints or mild cognitive impairment who were seeking care in memory clinics.

The current results suggest that the detrimental effect of diabetes on cognition is

mediated by neurodegeneration, independently of AD and small vessel disease

pathologies, in a population of older adults at risk for dementia. Longitudinal studies

are now needed to reinforce and confirm these findings.


TABLES

Table 1. Observed indicators for latent dimensions variables

Latent variables Observed indicators Data available

N (%)

Small vessel disease

White matter hyperintensities volume 1,884 (80.6%)

Fazekas scale scores for paraventricular

white matter hyperintensities 2,145 (93.8%)

Fazekas scale scores for deep white matter

hyperintensities 2,145 (93.8%)

Alzheimer’s disease

pathology

Mean brain amyloid uptake 643 (28.1%)

CSF Aβ42/Aβ40 ratio 400 (17.5%)

CSF Phosphorylated tau 408 (17.8%)

Neurodegeneration

Mean right cortical thickness 2,106 (92.0%)

Mean left cortical thickness 2,106 (92.0%)

Brain parenchymal fraction 2,103 (91.9%)

Hippocampal volume 2,061 (90.1%)

Mean brain FDG uptake 1,308 (57.2%)

Cognition

FCSRT total free recall score 2,269 (99.2%)

TMT A (seconds/correct move) 2,265 (99.0%)

TMT B (seconds/correct move) 2,192 (95.8%)

Rey complex figure test, 3-minute copy score 2,125 (92.9%)

Verbal fluency (number of animals produced) 2,245 (98.1%)

Abbreviations: FCSRT, Free and Cued Selective Reminding Test; TMT, Trail Making Test.


Table 2. Baseline characteristics associated with the availability of MRI, FDG-PET, amyloid-PET and CSF data – MEMENTO Study, France (n = 2,288).

Available data P a

No Yes

MRI, N 130 2,158

Cardiovascular history 20 (15.4) 185 (8.6) 0.008

MMSE score 27.4 (2.2) 27.9 (1.9) 0.001

FCSRT total free recall score 24.3 (9.2) 26.1 (8.2) 0.01

FDG-PET, N 980 1,308

Female sex 648 (66.1) 765 (58.5) <0.001

Current alcohol consumption 0.006

No 352 (37.1) 399 (30.8)

≤1d/day 412 (43.5) 604 (46.7)

>1d/day 184 (19.4) 291 (22.5)

Dyslipidemia 402 (55.0) 480 (46.3) <0.001

MMSE score 27.8 (2.0) 28.0 (1.9) 0.009

TMT A (seconds/correct move) 2.1 (1.0) 2.0 (0.9) 0.005

TMT B (seconds/correct move) 5.2 (3.6) 4.9 (3.2) 0.02

Rey complex figure test, 3-minute

copy score 14.5 (7.1) 15.6 (6.9) <0.001

Verbal fluency, animals (number of

words produced) 27.7 (8.7) 28.8 (8.7) 0.006

Amyloid -PET, N 1,645 643

Current alcohol consumption <0.001

No 584 (36.4) 167 (26.2)

≤1d/day 713 (44.5) 303 (47.5)

>1d/day 307 (19.1) 168 (26.3)

Diabetes 201 (12.2) 53 (8.2) 0.007

Dyslipidemia 642 (52.8) 240 (43.6) <0.001

Depression 677 (41.2) 212 (33.0) <0.001

Clinical Dementia Rating scale <0.001


0 540 (33.0) 383 (59.8)

0.5 1,096 (67.0) 258 (40.2)

MMSE score 27.7 (2.1) 28.3 (1.5) <0.001

FCSRT total free recall score 25.0 (8.6) 28.4 (6.9) <0.001

TMT A (seconds/correct move) 2.1 (1.0) 1.9 (0.7) <0.001

TMT B (seconds/correct move) 5.3 (3.6) 4.5 (2.7) <0.001

Rey complex fig ure test, 3 -minute

copy score 14.7 (7.1) 16.4 (6.6) <0.001


words produced) 27.5 (8.8) 30.3 (8.2) <0.001

CSF, N 1,877 411

Age (years) 71.3 (8.6) 68.8 (8.8) <0.001

Female sex 1197 (63.8) 216 (52.6) <0.001

Living alone 602 (32.4) 101 (24.6) 0.002

Physical activity, MET-hour/week 52.2 (47.2) 59.7 (52.9) 0.01

Clinical Dementia Rating scale 0.02

0 777 (41.6) 146 (35.5)

0.5 1089 (58.4) 265 (64.5)

APOE ε4 carrier 501 (28.0) 155 (38.9) <0.001

MMSE 27.9 (1.9) 27.7 (2.0) 0.001

FCSRT total free recall score 26.3 (8.2) 24.6 (8.8) <0.001


words produced) 28.4 (8.7) 27.9 (8.9) 0.04

Abbreviations: FCSRT, Free and Cued Selective Reminding Test; MET, metabolic equivalent of task; MMSE, Mini-Mental State Examination; TMT, Trail Making Test. a P-values for comparison using t-tests for quantitative variables and chi-square test or Fisher test for qualitative variables. Comparisons for cognitive tests were adjusted for age, sex and education.


Table 3. Baseline characteristics according to diabetes – MEMENTO Cohort, France (n = 2,288)

Diabetes

No

(n = 2,034)

Yes

(n = 254) P a

Age (years) 70.9 (8.8) 70.8 (7.9) 0.80

Female sex 1,302 (64.0) 111 (43.7) <0.001

Education 0.02

Low 487 (23.9) 71 (28.0)

Intermediate 722 (35.5) 103 (40.6)

High 823 (40.5) 80 (31.5)

Smoking status 0.05

Never 1,191 (59.0) 137 (54.8)

Former 676 (33.5) 101 (40.4)

Current 151 (7.5) 12 (4.8)

Current alcohol consumption 0.17

No 658 (33.0) 93 (37.8)

Up to 1 drink/day 918 (46.0) 98 (39.8)

>1 drink/day 420 (21.0) 55 (22.4)

Body mass index (kg/m²) <0.001

<20 145 (7.3) 6 (2.4)

20-25 910 (45.7) 68 (27.6)

25.1-29.9 712 (35.8) 92 (37.4)

≥30 223 (11.2) 80 (32.5)

Hypertension 1,135 (59.8) 188 (77.4) <0.001

Dyslipidemia 761 (48.9) 127 (60.5) 0.002

Self-reported cardiovascular history 156 (7.7) 49 (19.3) <0.001

Self-reported stroke history 76 (3.7) 16 (6.3) 0.05

Depression 791 (38.9) 98 (38.6) 0.92

APOE ε4 carrier 596 (30.6) 60 (24.6) 0.05

Cognitive tests

MMSE score 28.0 (1.9) 27.6 (2) 0.03 b

FCSRT total free recall score 26.2 (8.4) 24.2 (7.4) 0.03 b

TMT A (seconds/correct move) 2.05 (0.94) 2.16 (0.88) 0.02 c


TMT B (seconds/correct move) 4.97 (3.39) 5.57 (3.41) <0.001 d

Rey complex figure test, 3-minute

copy score 15.1 (7.0) 15.5 (7.0) 0.89 b

Verbal fluency, (number of animals

produced) 28.5 (8.7) 26.9 (8.7) 0.04 b

Missing data: education, 2; smoking status, 20; alcohol consumption, 46; body mass index, 52; hypertension, 148; dyslipidemia, 521; APOE genotype, 98; MMSE, 6; FCSRT, 19; TMT A, 23; TMT B, 96; Rey complex figure, 163; verbal fluency, 43. Abbreviations: FCSRT, Free and Cued Selective Reminding Test; MMSE, Mini-Mental State Examination; TMT, Trail Making Test. a P-values for comparison using t-tests for quantitative variables and chi-square test or Fisher test for qualitative variables, except when stated otherwise b P-values for comparison using linear regression modeling adjusted on age, sex and education. c P-value for comparison of log-transformed values of TMT A, adjusted on age, sex and education. d P-value for comparison of log-transformed values of TMT B, adjusted on age, sex and education.


Table 4. Association between diabetes, biomarkers of small vessel disease, neurodegeneration and Alzheimer’s disease, and specific cognitive domains – Structural equation model

Latent variable of memory

Latent variable of executive functioning

Standardized

estimate (95% CI)

P Standardized

estimate (95% CI)

P

Direct effect of diabetes on SVD 0.001 (-0.035; 0.037) 0.95 0.001 (-0.034; 0.037) 0.94 AD pathology 0.047 (-0.059; 0.153) 0.38 0.053 (-0.049; 0.155) 0.31 Neurodegeneration 0.108 (0.071; 0.145) <0.001 0.110 (0.074; 0.146) <0.001

Direct effect of

Diabetes on cognition 0.016 (-0.037; 0.069) 0.55 -0.017 (-0.070; 0.036) 0.53

SVD on cognition -0.104 (-0.169; -0.040) 0.001 -0.094 (-0.163; -0.024) 0.008

Neurodegeneration on cognition -0.542 (-0.737; -0.346) <0.001 -0.306 (-0.441; -

0.171) <0.001

AD pathology on cognition -0.282 (-0.421; -0.144) <0.001

-0.169 (-0.269; -0.068) 0.001

Correlation between

SVD and AD pathology 0.159 (0.064; 0.253) <0.001 0.151 (0.057; 0.245) 0.001 SVD and neurodegeneration 0.038 (-0.056; 0.133) 0.42 0.023 (-0.077; 0.123) 0.65 AD and neurodegeneration 0.257 (0.116; 0.398) <0.001 0.256 (0.128; 0.384) <0.001

Indirect effect of diabetes on cognition

Through SVD 0.000 (-0.004; 0.004) 0.95 0.000 (-0.003; 0.003) 0.94 Through AD pathology -0.013 (-0.042; 0.015) 0.36 -0.009 (-0.027; 0.010) 0.34

Through neurodegeneration -0.058 (-0.088; -0.029) <0.001 -0.034 (-0.051; -0.016) <0.001

Model fit indices Robust CFI 0.963 0.974 Robust TLI 0.937 0.956 Robust RSMEA (90% CI) 0.038 (0.035; 0.041) 0.032 (0.029; 0.035) p-value for test of close fit 1.00 1.00 SRMR 0.035 0.035

Abbreviations: AD, Alzheimer’s disease; CFI, comparative fit index; RSMEA, root mean square error of

approximation; SRMR, Standardized Root Mean Square Residual; SVD, small vessel disease; TLI, Tucker-Lewis

Index.


Table 5. Association between diabetes, biomarkers and global cognition - Sensitivity analyses

Excluding self-reported

history of

diabetes

Adding

auxiliary

variables

Excluding delayed

biomarker

measurements

(>6months)

Using only

brain

biomarkers

as indicators

Standardized

estimate

(95% CI)

P

Standardized

estimate

(95% CI)

P

Standardized

estimate

(95% CI)

P

Standardized

estimate

(95% CI)

P

Direct effect of

diabetes on

SVD -0.006

(-0.045; 0.033) 0.77

0.002

(-0.035; 0.038) 0.92

0.006

(-0.031; 0.043) 0.75

0.001

(-0.035; 0.036) 0.97

AD pathology 0.049

(-0.046; 0.143) 0.31

0.044

(-0.067; 0.155) 0.44

-0.007

(-0.172; 0.159) 0.94

0.107

(0.021; 0.193) 0.01

Neurodegeneration 0.084

(0.049; 0.121) <0.001

0.109

(0.072; 0.146) <0.001

0.106

(0.068; 0.144) <0.001

0.108

(0.071; 0.144) <0.001

Direct effect on

cognition of

Diabetes 0.030

(-0.017; 0.076) 0.21

0.023

(-0.030; 0.077) 0.39

0.012

(-0.047; 0.072) 0.69

0.030

(-0.020; 0.080) 0.23

SVD -0.114

(-0.185; -0.044) <0.001

-0.113

(-0.183; -0.043) 0.001

-0.108

(-0.187; -0.029) 0.007

-0.131

(-0.201; -0.061) <0.001

Neurodegeneration -0.576

(-0.743; -0.408) 0.001

-0.565

(-0.731; -0.399) <0.001

-0.601

(-0.765; -0.436) <0.001

-0.609

(-0.777; -0.442) <0.001

AD pathology -0.273

(-0.391; -0.154) <0.001

-0.275

(-0.403; -0.147) <0.001

-0.285

(-0.459; -0.111) 0.001

-0.144

(-0.248; -0.039) 0.007

Correlation

between

SVD and

AD pathology

0.157

(0.060; 0.254) 0.002

0.163

(0.066; 0.260) <0.001

0.161

(0.025; 0.298) 0.02

0.156

(0.054; 0.257) 0.003

SVD and

neurodegeneration

0.040

(-0.053; 0.134) 0.39

0.038

(-0.133; 0.057) 0.43

0.039

(-0.056; 0.134) 0.42

0.039

(-0.056; 0.133) 0.42

AD and

neurodegeneration

0.269

(0.130; 0.409) <0.001

0.259

(0.127; 0.390) <0.001

0.160

(0.004; 0.316) 0.04

0.236

(0.096; 0.376) 0.001

Indirect effect of

diabetes on

cognition

Through SVD 0.001

(-0.004; 0.005) 0.77

0.000

(-0.004; 0.004) 0.92

-0.001

(-0.005; 0.003) 0.75

0.000

(-0.005; 0.005) 0.97

Through AD

pathology

-0.013

(-0.037; 0.011) 0.28

-0.012

(-0.042; 0.018) 0.42

0.002

(-0.046; 0.049) 0.93

-0.015

(-0.033; 0.002) 0.08

Through

neurodegeneration

-0.048

(-0.075; -0.021) <0.001

-0.061

(-0.091; -0.032) <0.001

-0.064

(-0.093; -0.035) <0.001

-0.066

(-0.097; -0.034) <0.001

Model fit indices

Robust CFI 0.951 0.951 0.948 0.953

Robust TLI 0.926 0.926 0.921 0.924

Robust RSMEA

(90% CI)

0.040

(0.037; 0.042)

0.040

(0.037 ; 0.042)

0.040

(0.038 ; 0.043)

0.043

(0.040 ; 0.046)

p-value for test

of close fit 1.00 1.00 1.00 1.00

SRMR 0.038 0.032 0.042 0.033


Abbreviations: AD, Alzheimer’s disease; CFI, comparative fit index; RSMEA, root mean square error of

approximation; SRMR, Standardized Root Mean Square Residual; SVD, small vessel disease; TLI, Tucker-Lewis

Index.


FIGURE LEGEND

Figure 1. Structural equation model for the association between diabetes, small vessel disease, neurodegeneration, Alzheimer’s disease biomarkers and cognition

Latent variables of interest are indicated in ovals and observed variables in rectangles. Directed arrows represent linear regressions. Bidirectional arrows represent correlations. Standardized regression coefficients estimates are presented with their 95% confidence interval. Solid lines indicate statistically significant associations and correlations at the 5% level. Dotted lines indicate non-statistically significant associations and correlations at the 5% level. Adjustment covariates and their directed arrows to small vessel disease, neurodegeneration, Alzheimer’s disease biomarkers and cognition are represented in grey. For readiness, the observed indicators defining each latent variable (listed in Table 1) and residual variances for all variables were omitted. AD, Alzheimer’s disease. * p<0.001


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DOI 10.1212/WNL.0000000000012440 published online July 1, 2021Neurology

Eric Frison, Cecile Proust-Lima, Jean-Francois Mangin, et al. Diabetes Mellitus and Cognition: A Pathway Analysis in the MEMENTO Cohort

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